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A method of noise plus interference estimation for communication systems

Publishing Venue

The IP.com Prior Art Database

Abstract

Noise plus interference computation is key for any wireline/wireless communication system. With evolving standards such as LTE/LTE-A and others, there is a prominent need to correctly compute noise plus interference across multiple data and control channels. Noise plus interference is eventually used to compute Signal to Noise plus Interference Ratio (SINR), which is an essential part of any communication receiver. This paper describes an accurate and reliable method to compute noise plus interference under different multiplexing schemes such as Code Division Multiplexing (CDM), Frequency Division Multiplexing (FDM) and/or both CDM and FDM based users. A system level sensing is applied to obtain the details of resource allocation in time, frequency and code division that helps to identify used and unused allocations. This paper is not limited to estimate noise plus interference from unused time and/or frequency resources, but also considers scenarios where none of unused resources are available in time and frequency domain. The proposed method doesn’t put any constraint on higher layers for reserving any time and/or frequency resources and hence applicable for fully loaded system without compromising any system requirements such as capacity and throughput.

Country

India

Language

English (United States)

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This is the abbreviated version, containing approximately
39% of the total text.

A method of noise plus interference estimation for
communication systems

1. Abstract

Noise plus interference computation is key for any
wireline/wireless communication system. With evolving standards such as
LTE/LTE-A and others, there is a prominent need to correctly compute noise plus
interference across multiple data and control channels. Noise plus interference
is eventually used to compute Signal to Noise plus Interference Ratio (SINR),
which is an essential part of any communication receiver. This paper describes
an accurate and reliable method to compute noise plus interference under
different multiplexing schemes such as Code Division Multiplexing (CDM),
Frequency Division Multiplexing (FDM) and/or both CDM and FDM based users. A
system level sensing is applied to obtain the details of resource allocation in
time, frequency and code division that helps to identify used and unused
allocations. This paper is not limited to estimate noise plus interference from
unused time and/or frequency resources, but also considers scenarios where none
of unused resources are available in time and frequency domain. The proposed
method doesn’t put any constraint on higher layers for reserving any time
and/or frequency resources and hence applicable for fully loaded system without
compromising any system requirements such as capacity and throughput.

2. Overview of the proposed method

In communication system, it is required to estimate signal
to noise plus interference ratio (SINR) mainly because of following reasons:

At the receiver, there are multiple ways to estimate SINR
and that varies based on the standards and their applications. For example, a
single carrier based standard will estimate SINR differently compared to a
multi-carrier system. Similarly, a code division multiplexed signal will
experience a different SINR compared to an orthogonal-frequency-division
multiplexed signal due to coding gain at receiver. Additionally, in a given
standard, channels responsible to carry data, control,
sounding-reference-signal and synchronization signals vary in the way signal is
modulated. For an example, in LTE/LTE-A standard, a random-access channel is
time domain multiplexed but in sounding reference signal it is multiplexed in
both the domains i.e., time and
frequency. For a solution provider, it is required to design and maintain SINR
estimation schemes for each channel for an efficient functionality of the link.
To address above issues, there are following challenges and requirements:

 an optimum scheme to meet the algorithmic performance and
implementation complexity requirements